A behavioral comparison of male and female adults with high functioning autism spectrum conditions

Autism spectrum conditions (ASC) affect more males than females in the general population. However, within ASC it is unclear if there are phenotypic sex differences. Testing for similarities and differences between the sexes is important not only for clinical assessment but also has implications for theories of typical sex differences and of autism. Using cognitive and behavioral measures, we investigated similarities and differences between the sexes in age- and IQ-matched adults with ASC (high-functioning autism or Asperger syndrome). Of the 83 (45 males and 38 females) participants, 62 (33 males and 29 females) met Autism Diagnostic Interview-Revised (ADI-R) cut-off criteria for autism in childhood and were included in all subsequent analyses. The severity of childhood core autism symptoms did not differ between the sexes. Males and females also did not differ in self-reported empathy, systemizing, anxiety, depression, and obsessive-compulsive traits/symptoms or mentalizing performance. However, adult females with ASC showed more lifetime sensory symptoms (p = 0.036), fewer current socio-communication difficulties (p = 0.001), and more self-reported autistic traits (p = 0.012) than males. In addition, females with ASC who also had developmental language delay had lower current performance IQ than those without developmental language delay (p<0.001), a pattern not seen in males. The absence of typical sex differences in empathizing-systemizing profiles within the autism spectrum confirms a prediction from the extreme male brain theory. Behavioral sex differences within ASC may also reflect different developmental mechanisms between males and females with ASC. We discuss the importance of the superficially better socio-communication ability in adult females with ASC in terms of why females with ASC may more often go under-recognized, and receive their diagnosis later, than males.

Longitudinal investigation of the role of temperament and stressful life events in childhood anxiety

The current study investigated the longitudinal relationships between BI, life events, and anxiety in a sample of 102 behaviourally inhibited (BI) and 100 uninhibited (BUI) children aged 3 to 4 years. Children’s parents completed questionnaires on BI, stressful life events, and anxiety symptoms, and were administered a diagnostic interview three times in a 5-year period. In line with our hypotheses, negative life events, and negative behaviour- dependent life events (i.e. life events that are related to the children’s own behaviours) in particular, and the impact of negative life events, were predictive of increases in subsequent anxiety symptoms, the likelihood of having an anxiety disorder, and increased number of anxiety diagnoses over the five year follow-up period. Experiencing more positive, behaviour-independent life events decreased the risk of being diagnosed with an anxiety disorder. Furthermore, differences were found in life events between BI and BUI children. That is, BI children experienced fewer positive and specifically positive behaviour-dependent life events, and the impact of these positive life events was also lower in BI children than in BUI children. However, BI did not interact with life events in the prediction of anxiety problems as hypothesized. Therefore, this study seems to indicate that BI and life events act as additive risk factors in the development of anxiety problems.

The relationship between challenging parenting behaviour and childhood anxiety disorders

Background: This research investigates the relationship between challenging parenting behaviour and childhood anxiety disorders proposed by Bögels and Phares (2008). Challenging parenting behaviour involves the playful encouragement of children to go beyond their own limits, and may decrease children’s risk for anxiety (Bögels & Phares, 2008). Method: Parents (n = 164 mothers, 144 fathers) of 164 children aged between 3.4 and 4.8 years participated in the current study. A multi-method, multi-informant assessment of anxiety was used, incorporating data from diagnostic interviews as well as questionnaire measures. Parents completed self-report measures of their parenting behaviour (n = 147 mothers, 138 fathers) and anxiety (n = 154 mothers, 143 fathers). Mothers reported on their child’s anxiety via questionnaire as well as diagnostic interview (n = 156 and 164 respectively). Of these children, 74 met criteria for an anxiety disorder and 90 did not. Results: Fathers engaged in challenging parenting behaviour more often than mothers. Both mothers’ and fathers’ challenging parenting behaviour was associated with lower report of child anxiety symptoms. However, only mothers’ challenging parenting behaviour was found to predict child clinical anxiety diagnosis. Limitations: Shared method variance from mothers confined the interpretation of these results. Moreover, due to study design, it is not possible to delineate cause and effect. Conclusions: The finding with respect to maternal challenging parenting behaviour was not anticipated, prompting replication of these results. Future research should investigate the role of challenging parenting behaviour by both caregivers as this may have implications for parenting interventions for anxious children.

Diagnostic analysis of atmospheric moisture and clear-sky radiative feedback in the Hadley Centre and Geophysical Fluid Dynamics Laboratory (GFDL) climate models

The interannual variability of the hydrological cycle is diagnosed from the Hadley Centre and Geophysical Fluid Dynamics Laboratory (GFDL) climate models, both of which are forced by observed sea surface temperatures. The models produce a similar sensitivity of clear-sky outgoing longwave radiation to surface temperature of ∼2 W m−2 K−1, indicating a consistent and positive clear-sky radiative feedback. However, differences between changes in the temperature lapse-rate and the height dependence of moisture fluctuations suggest that contrasting mechanisms bring about this result. The GFDL model appears to give a weaker water vapor feedback (i.e., changes in specific humidity). This is counteracted by a smaller upper tropospheric temperature response to surface warming, which implies a compensating positive lapse-rate feedback.

A capture–recapture approach for screening using two diagnostic tests with availability of disease status for the test positives only

The article considers screening human populations with two screening tests. If any of the two tests is positive, then full evaluation of the disease status is undertaken; however, if both diagnostic tests are negative, then disease status remains unknown. This procedure leads to a data constellation in which, for each disease status, the 2 × 2 table associated with the two diagnostic tests used in screening has exactly one empty, unknown cell. To estimate the unobserved cell counts, previous approaches assume independence of the two diagnostic tests and use specific models, including the special mixture model of Walter or unconstrained capture–recapture estimates. Often, as is also demonstrated in this article by means of a simple test, the independence of the two screening tests is not supported by the data. Two new estimators are suggested that allow associations of the screening test, although the form of association must be assumed to be homogeneous over disease status. These estimators are modifications of the simple capture–recapture estimator and easy to construct. The estimators are investigated for several screening studies with fully evaluated disease status in which the superior behavior of the new estimators compared to the previous conventional ones can be shown. Finally, the performance of the new estimators is compared with maximum likelihood estimators, which are more difficult to obtain in these models. The results indicate the loss of efficiency as minor.

Revisiting youden’s index as a useful measure of the misclassification error in meta-analysis of diagnostic studies

The paper considers meta-analysis of diagnostic studies that use a continuous score for classification of study participants into healthy or diseased groups. Classification is often done on the basis of a threshold or cut-off value, which might vary between studies. Consequently, conventional meta-analysis methodology focusing solely on separate analysis of sensitivity and specificity might be confounded by a potentially unknown variation of the cut-off value. To cope with this phenomena it is suggested to use, instead, an overall estimate of the misclassification error previously suggested and used as Youden’s index and; furthermore, it is argued that this index is less prone to between-study variation of cut-off values. A simple Mantel–Haenszel estimator as a summary measure of the overall misclassification error is suggested, which adjusts for a potential study effect. The measure of the misclassification error based on Youden’s index is advantageous in that it easily allows an extension to a likelihood approach, which is then able to cope with unobserved heterogeneity via a nonparametric mixture model. All methods are illustrated at hand of an example on a diagnostic meta-analysis on duplex doppler ultrasound, with angiography as the standard for stroke prevention.

Chlorophyll fluorescence technique as a rapid diagnostic test of the effects of the photosynthetic inhibitor chlorotoluron on two winter wheat cultivars

A modified chlorophyll fluorescence technique was evaluated as a rapid diagnostic test of the susceptibility of wheat cultivars to chlorotoluron. Two winter wheat cultivars (Maris Huntsman and Mercia) exhibited differential response to the herbicide. All of the parameters of chlorophyll fluorescence examined were strongly influenced by herbicide concentration. Additionally, the procedure adopted here for the examination of winter wheat cultivar sensitivity to herbicide indicated that the area above the fluorescence induction curve and the ratio F-V/F-M are appropriate chlorophyll fluorescence parameters for detection of differential herbicide response between wheat cultivars. The potential use of this technique as an alternative to traditional methods of screening new winter wheat cultivars for their response to photosynthetic inhibitor herbicide is demonstrated here.

Influence-matrix diagnostic of a data assimilation system

The influence matrix is used in ordinary least-squares applications for monitoring statistical multiple-regression analyses. Concepts related to the influence matrix provide diagnostics on the influence of individual data on the analysis - the analysis change that would occur by leaving one observation out, and the effective information content (degrees of freedom for signal) in any sub-set of the analysed data. In this paper, the corresponding concepts have been derived in the context of linear statistical data assimilation in numerical weather prediction. An approximate method to compute the diagonal elements of the influence matrix (the self-sensitivities) has been developed for a large-dimension variational data assimilation system (the four-dimensional variational system of the European Centre for Medium-Range Weather Forecasts). Results show that, in the boreal spring 2003 operational system, 15% of the global influence is due to the assimilated observations in any one analysis, and the complementary 85% is the influence of the prior (background) information, a short-range forecast containing information from earlier assimilated observations. About 25% of the observational information is currently provided by surface-based observing systems, and 75% by satellite systems. Low-influence data points usually occur in data-rich areas, while high-influence data points are in data-sparse areas or in dynamically active regions. Background-error correlations also play an important role: high correlation diminishes the observation influence and amplifies the importance of the surrounding real and pseudo observations (prior information in observation space). Incorrect specifications of background and observation-error covariance matrices can be identified, interpreted and better understood by the use of influence-matrix diagnostics for the variety of observation types and observed variables used in the data assimilation system. Copyright © 2004 Royal Meteorological Society

Revisiting Youden's index as a useful measure of the misclassification error in meta-analysis of diagnostic studies

The paper considers meta-analysis of diagnostic studies that use a continuous Score for classification of study participants into healthy, or diseased groups. Classification is often done on the basis of a threshold or cut-off value, which might vary between Studies. Consequently, conventional meta-analysis methodology focusing solely on separate analysis of sensitivity and specificity might he confounded by a potentially unknown variation of the cut-off Value. To cope with this phenomena it is suggested to use, instead an overall estimate of the misclassification error previously suggested and used as Youden's index and; furthermore, it is argued that this index is less prone to between-study variation of cut-off values. A simple Mantel-Haenszel estimator as a summary measure of the overall misclassification error is suggested, which adjusts for a potential study effect. The measure of the misclassification error based on Youden's index is advantageous in that it easily allows an extension to a likelihood approach, which is then able to cope with unobserved heterogeneity via a nonparametric mixture model. All methods are illustrated at hand of an example on a diagnostic meta-analysis on duplex doppler ultrasound, with angiography as the standard for stroke prevention.

A capture-recapture approach for screening using two diagnostic tests with availability of disease status for the test positives only

The article considers screening human populations with two screening tests. If any of the two tests is positive, then full evaluation of the disease status is undertaken; however, if both diagnostic tests are negative, then disease status remains unknown. This procedure leads to a data constellation in which, for each disease status, the 2 x 2 table associated with the two diagnostic tests used in screening has exactly one empty, unknown cell. To estimate the unobserved cell counts, previous approaches assume independence of the two diagnostic tests and use specific models, including the special mixture model of Walter or unconstrained capture-recapture estimates. Often, as is also demonstrated in this article by means of a simple test, the independence of the two screening tests is not supported by the data. Two new estimators are suggested that allow associations of the screening test, although the form of association must be assumed to be homogeneous over disease status. These estimators are modifications of the simple capture-recapture estimator and easy to construct. The estimators are investigated for several screening studies with fully evaluated disease status in which the superior behavior of the new estimators compared to the previous conventional ones can be shown. Finally, the performance of the new estimators is compared with maximum likelihood estimators, which are more difficult to obtain in these models. The results indicate the loss of efficiency as minor.